Struct google_ml1_beta1::GoogleCloudMlV1beta1__TrainingInput
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pub struct GoogleCloudMlV1beta1__TrainingInput { pub worker_type: Option<String>, pub runtime_version: Option<String>, pub scale_tier: Option<String>, pub master_type: Option<String>, pub hyperparameters: Option<GoogleCloudMlV1beta1__HyperparameterSpec>, pub region: Option<String>, pub args: Option<Vec<String>>, pub python_module: Option<String>, pub job_dir: Option<String>, pub package_uris: Option<Vec<String>>, pub worker_count: Option<i64>, pub parameter_server_type: Option<String>, pub parameter_server_count: Option<i64>, }
Represents input parameters for a training job.
This type is not used in any activity, and only used as part of another schema.
Fields
worker_type: Option<String>
Optional. Specifies the type of virtual machine to use for your training job's worker nodes.
The supported values are the same as those described in the entry for
masterType
.
This value must be present when scaleTier
is set to CUSTOM
and
workerCount
is greater than zero.
runtime_version: Option<String>
Optional. The Google Cloud ML runtime version to use for training. If not set, Google Cloud ML will choose the latest stable version.
scale_tier: Option<String>
Required. Specifies the machine types, the number of replicas for workers and parameter servers.
master_type: Option<String>
Optional. Specifies the type of virtual machine to use for your training job's master worker.
The following types are supported:
- standard
- A basic machine configuration suitable for training simple models with small to moderate datasets.
- large_model
- A machine with a lot of memory, specially suited for parameter servers when your model is large (having many hidden layers or layers with very large numbers of nodes).
- complex_model_s
- A machine suitable for the master and workers of the cluster when your model requires more computation than the standard machine can handle satisfactorily.
- complex_model_m
-
A machine with roughly twice the number of cores and roughly double the
memory of
complex_model_s
. - complex_model_l
-
A machine with roughly twice the number of cores and roughly double the
memory of
complex_model_m
. - standard_gpu
-
A machine equivalent to
standard
that also includes a GPU that you can use in your trainer. - complex_model_m_gpu
-
A machine equivalent to
complex_model_m
that also includes four GPUs.
You must set this value when scaleTier
is set to CUSTOM
.
hyperparameters: Option<GoogleCloudMlV1beta1__HyperparameterSpec>
Optional. The set of Hyperparameters to tune.
region: Option<String>
Required. The Google Compute Engine region to run the training job in.
args: Option<Vec<String>>
Optional. Command line arguments to pass to the program.
python_module: Option<String>
Required. The Python module name to run after installing the packages.
job_dir: Option<String>
Optional. A Google Cloud Storage path in which to store training outputs and other data needed for training. This path is passed to your TensorFlow program as the 'job_dir' command-line argument. The benefit of specifying this field is that Cloud ML validates the path for use in training.
package_uris: Option<Vec<String>>
Required. The Google Cloud Storage location of the packages with the training program and any additional dependencies. The maximum number of package URIs is 100.
worker_count: Option<i64>
Optional. The number of worker replicas to use for the training job. Each
replica in the cluster will be of the type specified in worker_type
.
This value can only be used when scale_tier
is set to CUSTOM
. If you
set this value, you must also set worker_type
.
parameter_server_type: Option<String>
Optional. Specifies the type of virtual machine to use for your training job's parameter server.
The supported values are the same as those described in the entry for
master_type
.
This value must be present when scaleTier
is set to CUSTOM
and
parameter_server_count
is greater than zero.
parameter_server_count: Option<i64>
Optional. The number of parameter server replicas to use for the training
job. Each replica in the cluster will be of the type specified in
parameter_server_type
.
This value can only be used when scale_tier
is set to CUSTOM
.If you
set this value, you must also set parameter_server_type
.
Trait Implementations
impl Default for GoogleCloudMlV1beta1__TrainingInput
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fn default() -> GoogleCloudMlV1beta1__TrainingInput
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Returns the "default value" for a type. Read more
impl Clone for GoogleCloudMlV1beta1__TrainingInput
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fn clone(&self) -> GoogleCloudMlV1beta1__TrainingInput
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Returns a copy of the value. Read more
fn clone_from(&mut self, source: &Self)
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Performs copy-assignment from source
. Read more